C.S. 793
Data Mining
for
Commodity Trading
Notes on Commodity Trading
Also consult the many text (not required
to buy) shown on the syllabus.
Since then, competitive exchanges have continued to reduce latency with turnaround times of 3 milliseconds
available. This is of great importance to high-frequency traders because they have to attempt to pinpoint the
consistent and probable performance ranges of financial instruments. These professionals are often dealing in
versions of stock index funds like the E-mini S&Ps because they seek consistency and risk-mitigation along
with top performance. They must filter market data to work into their software prog so lowest latency and
highest liquidity at the time for placing stop-losses and/or taking profits. With high volatility, this becomes a
complex and potentially nerve-wracking endeavor, where a small mistake can lead to a large loss. Absolute
frequency data play into the development of the trader's pre-programmed instructs.[54] Spending on
comps/sftwre in financial industry increased to $26.4B in 2005.[1]
Communication standards Algorithmic trades require communicating considerably more parameters than
traditional market and limit orders. A trader on one end (the "buy side") must enable their trading system
(often called an "Order Management System" or "Execution Management System") to understand a
constantly proliferating flow of new algorithmic order types. The R&D and other costs to construct complex
new algorithmic orders types, along with the execution infrastructure, and marketing costs to distribute them,
are fairly substantial. What was needed was a way that marketers (the "sell side") could express algo orders
elect. such that buy-side traders could just drop the new order types into their system and be ready to trade
them w/o constant coding new order entry screens each time. FIX Protocol LTD
http://www.fixprotocol.org is a trade association that publishes free, open standards in the securities
trading area. The FIX language was originally created by Fidelity Investments, and the association
Members include virtually all large and many midsized and smaller broker dealers, money center banks,
institutional investors, mutual funds, etc. This institution dominates standard setting in pretrade and trade
areas of security transactions. 06-07 several members got together and published a draft XML standard for
expressing algo order types. Standard FIX Algorithmic Trading Defi Lang (FIXatdl).[55] V1.0 was not
widely adopted due to limitations in specs, but, 1.1 (released in March 2010) is expected to achieve broad
adoption and in the process dramatically reduce time-to-market and costs assoc. w distrib new algs.
Editing Algorithmic trading (section)
From Wikipedia, the free encyclopedia You are not currently
logged in. If you save any edits, your IP address will be recorded publicly in this page's edit history. If you create
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Financial Info eXchange ("FIX") Protocol is a series of messaging specifications for electronic comm of trade-related
messages. Developed thru the collaboration of banks, broker-dealers, exchanges, industry utilities and associations,
institutional investors, and information technology providers from around the world. These market participants share a
vision of a common, global language for the automated trading of financial instruments. FIX is the industry-driven
messaging standard that is changing the face of the global financial services sector, as firms use the protocol to transact
in an electronic, transparent, cost efficient and timely manner. FIX is open and free, but it is not software. Rather, FIX
is a specification around which software developers can create commercial or open-source software, as they see fit. As
market's leading trade-communications protocol, FIX is integral to many order mgmnt and trading sys
-TILT2-step
2200 BTU's
4-Wheel Drive
60-Step
The Abyss
Algo Mountains
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Apollo
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The Awakening
Back to School
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The Click
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Control Tower
Crazy Eyes
The Crown
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Day Trippin
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The Deep
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Depth Ping
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EPIC Zapper
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Focus
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Fred
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The Search
Search Bots
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Seen Too Much
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Simple BAT
Single Track
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Soylent Blue
The Spartan
Spastic BAT
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Stubby Triangles
Sunshowers
T1 Killer
Take Two
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Tesla's Cathedral
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Them
tHigh EQ
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Thin Blue Line
Things that make
The Tickler
To The Moon, Alice!
Twilight
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Wild Thing
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You Don't Know Jack
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The Zanti Misfit
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Zero to Sixty
In electronic financial markets, algorithmic trading or automated trading, also known as algo
trading, black-box trading or robo trading, is the use of computer programs for entering trading
orders with the computer alg deciding timing, price, or quantity of the order, or in many cases
initiating the order w/o human intervention. Algo Trading is widely used by pension funds, mutual
funds, and other buy side (investor driven) institutional traders, to divide large trades into several
smaller trades in order to manage market impact/risk.[1][2] Sell side traders (market makers, hedge
funds, provide liquidity to the market, generating and executing orders automatically.
A special class of algo trading is high-frequency trading (HFT), in which computers make decisions
to initiate orders based on information that is received electronically, before human traders are
capable of processing the information they observe. This has resulted in a dramatic change of the
market microstructure, particularly in the way liquidity is provided
Algo trading may be used in market making, inter-market spreading, arbitrage, or pure speculation
(including trend following). The investment decision and impl. may be augmented at any stage with
algorithmic support or may operate completely automatically ("on auto-pilot"). In 09, HFT was 73%
of all US equity trading volume.[5].
One of the main issues regarding HFT is the difficulty in determining how profitable it is. A 09
report by TABB estimated that the 300 securities firms and hedge funds that specialize in this type of
trading took in roughly $21 billion in profits in 2008.[10].
Algorithmic and HFT have been the subject of much public debate since the SEC and the Commodity
Futures Trading Commission implicated them in the May 6, 2010 Flash Crash, [11] [12] [13]
[14][15][16][17][18] when DJIA had its 2nd largest intraday point swing ever
History: Computerization of order flow began in 1970s with NYStockEx designated order turnaround system,
routing orders electronically to be executed manually, opening auto rpting sys (OARS), aided in market clearing
opening price (Smart Order Routing). Program trading is an order to buy/sell 15 stocks at > $1M total.
Stock index arbitrage traders buy/sell stock index futures contracts, sell/buy portfolios of 500 stocks matched
against the futures trade.
Portfolio insurance created a synthetic put option on a stock portfolio by dynamically trading stock index futures
according to a computer model based on the Black-Scholes option pricing model.
Both strategies were blamed for 87 stock market crash. Yet the impact of computer driven trading on crashes is
unclear.[21] In the U.S., decimalization, changed the minimum tick size from $.0625 to $.01 per share, may have
encouraged algo trading. This increased market liquidity led to inst'l traders splitting up orders according to computer
algs to execute their orders at better avg price
Average price is calculated by computers by applying time weighted (i.e. unweighted) avg price TWAP or volume
wtd avg price VWAP. As more elec markets opened, other algo trading strategies appeared. These strategies are more
easily implemented by computers because machines can react more rapidly to temporary mis-pricing and examine
prices from several markets simultaneously. E.g. Stealth (developed by the Deutsche Bank), Sniper and Guerilla
(developed by Credit Suisse[22]), arbitrage, statistical arbitrage, trend following, and mean reversion. This type of
trading is what is driving the new demand for Low Latency Proximity Hosting and Global Exchange Connectivity.
It is imperative to understand what is latency when putting together a strategy for electronic trading. Latency refers to
the delay between the transmission of info from a source and the reception of the information at a destination.
Latency has as a lower bound the speed of light; this corresponds to about 3.3 milliseconds per 1,000 kilometers of
optical fibre.Any signal regenerating equip will introduce greater latency than this speed-of-light baseline.
Strategies: Trend Following tries to take advantage of long-term moves that seem to play out in various markets. The
system aims to work on the market trend mechanism and take benefit from both sides of the market enjoying the profits
from the ups and downs of the stock or futures markets. Traders use current market price calc, moving avgs and channel
breakouts to determine direction and generate trade signals. Traders jump on the trend and ride it.
Pair Trading traders profit from any market cond: uptrend, downtrend, sidewise movement.
Delta Neutral Strategies is a portfolio of related securities whose value remains unchanged due to small changes in value
of underlying security.
Arbitrage: In economics and finance, arbitrage is the practice of taking advantage of a price difference between two or
more markets: striking a combination of matching deals that capitalize upon the imbalance, the profit being the difference
between the market prices. In academics, an arbitrage is a transaction that involves no negative cash flow at any
probabilistic/temporal state and positive cash flow in 1 state, it is the possibility of a risk-free profit at zero cost.
Conditions for arbitrage: Arbitrage is possible when 1 of these is met The same asset doesn't trade at the same price on
all markets - The law of one price. Two assets with identical cash flows don't trade at the same price. Asset with a known
price in future does not today trade at its future price discounted at a risk-free interest rate (or the asset doesn't have
negligible costs of storage; eg, this cond holds for grain but not for securities). Arbitrage is buying a product in 1 market
and selling it in another for a high price at a later time. The transaction must occur simultaneously to avoid exposure to
market risk (prices change before trans is done). In the simplest example, any good sold in one market should sell for the
same price in another. Traders may, eg, find wheat prices lower in ag regions than cities, purchase the good, and transport
it to another region to sell at a higher price (ignores transport, storage, risk, etc.). True arbitrage requires that there be no
market risk involved. Where securities are traded on more than one exchange, arbitrage occurs by simultaneously buying
in one and selling on the other.
Mean reversion: identify the trading range for a stock, compute the avg price wrt assets, earnings, etc. When current
price < avg price, stock is considered attractive for purchase - expecting the price will rise. When market price > average
price, market price expected to fall. I.e., deviations from avg price are expected to revert to the average. STD of most
recent prices (e.g., last 20) is often a buy/sell indicator. Stock reporting services (Yahoo! Finance, MS Investor,
Morningstar, etc.), offer moving avgs for periods eg 50,100 days. Identifying high/low prices for the study period is still
necessary.
Scalping (trading) is a method of arbitrage of small price gaps created by the bid-ask spread.
Scalpers attempt to act like traditional market makers or specialists. To make the spread means
to buy at the Bid price and sell at the Ask price, to gain the bid/ask difference. This procedure
allows for profit even when the bid and ask do not move at all, if traders are willing to take
market prices. It normally involves establishing and liquidating a position quickly-in min/sec. A
scalper is a market makers who maintains liquidity and order flow of a product of a market. A
market maker is a specialized scalper (volume of trades are much more than the scalpers.) and
has a trading sys to monitor activity, bound by exchange rules while individ trader is not.
Transaction cost reduction: Most Algo Trading strategies are cost-reducers. Large orders are
broken into smaller orders and entered over time ("iceberging"). Success is measured by avg
purchase price against VWAP. Alg design finds hidden orders or icebergs is called "Stealth".
Strategies that only pertain to dark pools: Recently, HFT, comprises a broad set of buyside
as well as market making sell side traders has become prominent and controversial.[25] These
algs or techniques are commonly given names eg "Stealth" (devel by Deutsche Bank), "Iceberg",
"Dagger", "Guerrilla", "Sniper", "BASOR" Dark pools are alternative elect stock exchanges
where trading is anonymous, with most orders hidden or "iceberged."[28]. Gamers or "sharks"
sniff out large orders by "pinging" small market orders to buy and sell. When several small
orders are filled sharks discover presence of a large iceberged order. "It’s an arms race,”
Everyone is building more sophisticated algs.
High-frequency trading: HFT firms represent 2% of the approx 20,000 firms operating today, but account for 73%
of all equity trading volume.[31] As of the first quarter in 2009, total assets under management for hedge funds with
HFT strategies were US$141 billion, down about 21% from their high.[32].
The HFT strategy was first made successful by Renaissance Technologies.[33] High-frequency funds started to
become especially popular in 2007 and 2008.[32] Many HFT firms are market makers and provide liquidity to the
market which has lowered volatility and helped narrow Bid-offer spreads making trading and investing cheaper for
other market participants. [32] [34] [35]
HFT has been a subject of intense public focus since the U.S. SEC and the Commodity Futures Trading
Commission implicated both algorithmic and HFT in the May 6, 2010 Flash Crash.[11][12][13][14].
HFT is quantitative trading that is characterized by short portfolio holding periods (Wilmott (2008), Aldridge
(2009)). There are 4 categories of HFT strategies: market-making based on order flow, market-making based on tick
data info, event arbitrage & statistical arbitrage.All portfolio allocation decisions are made by computerized
quantitative models.
The success of HFT strategies is largely driven by their ability to simultaneously process volumes of info,
something ordinary human traders cannot do.
Market making is a set of HFT strategies involving placing a limit order to sell (or offer) above the current market
price or a buy limit order (or bid) below the current price in order to benefit from the bid-ask spread. AutoTrading
Desk, bought by Citigroup 7/07, has been an active market maker accounting for 6% of total volume on both
NASDAQ &NY Stock Exchange.[36]
Statistical Arbitrage: Classical arbitrage strategy might involve several securities such as covered interest rate
parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond
denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the
currency. If the market prices are sufficiently different from those implied in the model to cover transaction cost
then 4 transactions can be made to guarantee a risk-free profit. HFT allows similar arbitrages using models of
greater complexity involving many more than 4 securities. The TABB Group estimates that annual aggregate profits
of low latency arbitrage currently exceed $21 billion.[5]
Event Arbitrage: A subset of risk, merger, convertible, or distressed securities arbitrage that counts
on a specific event (contract signing, regulatory approval, judicial decision...) to change price of two
or more financial instruments and permit the arbitrageur to earn a profit.[37].
Merger arbitrage also called risk arbitrage would be an example of this. Merger arbitrage generally
consists of buying the stock of a company that is the target of a takeover while shorting the stock of
acquiring company. Usually the market price of the target company is less than the price offered by
the acquiring company. The spread between these two prices depends mainly on the probability and
the timing of the takeover being completed as well as the prevailing level of interest rates. The bet in
a merger arbitrage is such a spread will eventually be zero, if and when the takeover is completed.
The risk is that the deal "breaks" and the spread massively widens.
Low-latency trading: HFT is often confused with lo-latency trading that uses computers that
execute trades within ms, or "with extremely low latency" in jargon of trade. Lo-latency trading is
highly dependent on ultra-low latency networks. They profit by providing info, such as competing
bids and offers, to their algs ms faster than their competitors.[5] The revolutionary advance in speed
has led to the need for firms to have a real-time, colocated trading platform in order to benefit from
impl high- frequency strategies.[5] Strategies are constantly altered to reflect the subtle changes in the
market as well as to combat the threat of the strategy being reverse engineered by competitors. There's
also a very strong pressure to continuously add features or improvements to a particular alg, such as
client specific modifications and various perf. enhancing changes (benchmark trading perf, cost
reduction for the trading firm or a range of other impls). This is due to the evolutionary nature of
algorithmic trading strategies - they must be able to adapt and trade intelligently, regardless of market
conditions which involves being flexible enough to withstand a vast array of market scenarios. As a
result, a significant proportion of net revenue from firms is spent on R&D of these autonomous
trading sys.[5].
Strategy Implementation: Most algo strategies implemented using modern prog langs, tho some
still implement strategies designed in spreadsheets. Basic models can rely on as little as a linear
regression, while more complex game-theoretic and pattern recognition[38] or predictive models
can also be used to initiate trading. Neural nets and GAs used to create these models.
Issues and developments: Algo trading improves market liquidity[39] among other benefits.
Improvements in productivity brought by algo trading have been opposed by human
brokers/traders facing competition from computers
Concerns: “The downside with these systems is their black box-ness,” Mr. Williams said.
“Traders have intuitive senses of how the world works. But with these systems you pour in a bunch
of numbers, and something comes out the other end, and it’s not always intuitive or clear why the
black box latched onto certain data or relationships.”[29] “The Financial Services Authority has
been keeping a watchful eye on the development of black box trading. In its annual report the
regulator remarked on the great benefits of efficiency that new technology is bringing to the
market. But it also pointed out that ‘greater reliance on sophisticated technology and modeling
brings with it a greater risk that systems failure can result in bus interruption’. ”[40]. UK Treasury
minister Lord Myners warned companies could become "playthings" of speculators because of
automatic high-frequency trading.
Lord Myners said the process risked destroying the relationship between an investor and a
company.[41]. Other issues include the technical problem of latency or the delay in getting quotes
to traders,[42] security and the possibility of a complete system breakdown leading to a market
crash.[43] Algorithmic and HFT were implicated in the May 6, 2010 Flash Crash,[11][13] when
the DJIA plunged about 600 points only to recover those losses within minutes. At the time, it was
2nd largest point swing, 1,010.14 points, and the biggest one-day point decline, 998.5 points, on an
intraday basis in DJIA history.
In electronic financial markets, algorithmic trading or automated trading, also known as algo
trading, black-box trading or robo trading, is the use of computer programs for entering trading
orders with the computer alg deciding timing, price, or quantity of the order, or in many cases
initiating the order w/o human intervention.
Algo Trading is widely used by pension funds, mutual funds, and other buy side (investor driven)
institutional traders, to divide large trades into several smaller trades in order to manage market
impact/risk.[1][2] Sell side traders (market makers, hedge funds, provide liquidity to the market,
generating and executing orders automatically.
A special class of algo trading is high-frequency trading (HFT), in which computers make decisions
to initiate orders based on information that is received electronically, before human traders are
capable of processing the information they observe. This has resulted in a dramatic change of the
market microstructure, particularly in the way liquidity is provided
Algo trading may be used in market making, inter-market spreading, arbitrage, or pure speculation
(including trend following). The investment decision and impl. may be augmented at any stage with
algorithmic support or may operate completely automatically ("on auto-pilot"). In 09, HFT was 73%
of all US equity trading volume.[5].
One of the main issues regarding HFT is the difficulty in determining how profitable it is. A 09
report by TABB estimated that the 300 securities firms and hedge funds that specialize in this type of
trading took in roughly $21 billion in profits in 2008.[10].
Algorithmic and HFT have been the subject of much public debate since the SEC and the Commodity
Futures Trading Commission implicated them in the May 6, 2010 Flash Crash, [11] [12] [13]
[14][15][16][17][18] when DJIA had its 2nd largest intraday point swing ever
History: Computerization of order flow began in 1970s with NYStockEx designated order
turnaround system, routing orders electronically to be executed manually, opening auto rpting sys
(OARS), aided in market clearing opening price (Smart Order Routing).
Program trading is an order to buy/sell 15 stocks at > $1M total.
Stock index arbitrage traders buy/sell stock index futures contracts, sell/buy portfolios of 500
stocks matched against the futures trade.
Portfolio insurance created a synthetic put option on a stock portfolio by dynamically trading
stock index futures according to a computer model based on the Black-Scholes option pricing
model.
Both strategies were blamed for 87 stock market crash. Yet the impact of computer driven trading
on crashes is unclear.[21] In the U.S., decimalization, changed the minimum tick size from $.0625
to $.01 per share, may have encouraged algo trading. This increased market liquidity led to inst'l
traders splitting up orders according to computer algs to execute their orders at better avg price
Average price is calculated by computers by applying time weighted (i.e. unweighted) avg price
TWAP or volume wtd avg price VWAP. As more elec markets opened, other algo trading strategies
appeared. These strategies are more easily implemented by computers because machines can react
more rapidly to temporary mis-pricing and examine prices from several markets simultaneously.
E.g. Stealth (developed by the Deutsche Bank), Sniper and Guerilla (developed by Credit
Suisse[22]), arbitrage, statistical arbitrage, trend following, and mean reversion. This type of
trading is what is driving the new demand for Low Latency Proximity Hosting and Global
Exchange Connectivity.
It is imperative to understand what is latency when putting together a strategy for electronic
trading. Latency refers to the delay between the transmission of info from a source and the
reception of the information at a destination. Latency has as a lower bound the speed of light; this
corresponds to about 3.3 milliseconds per 1,000 kilometers of optical fibre.Any signal regenerating
equip will introduce greater latency than this speed-of-light baseline.
Strategies:
Trend Following tries to take advantage of long-term moves that seem to play out in various markets. The system
aims to work on the market trend mechanism and take benefit from both sides of the market enjoying the profits
from the ups and downs of the stock or futures markets. Traders use current market price calc, moving avgs and
channel breakouts to determine direction and generate trade signals. Traders jump on the trend and ride it.
Pair Trading traders profit from any market cond: uptrend, downtrend, sidewise movement.
Delta Neutral Strategies is a portfolio of related securities whose value remains unchanged due to small changes
in value of underlying security.
Arbitrage: In economics and finance, arbitrage is the practice of taking advantage of a price difference between
two or more markets: striking a combination of matching deals that capitalize upon the imbalance, the profit being
the difference between the market prices. In academics, an arbitrage is a transaction that involves no negative cash
flow at any probabilistic/temporal state and positive cash flow in 1 state, it is the possibility of a risk-free profit at
zero cost.
Conditions for arbitrage: Arbitrage is possible when 1 of these is met The same asset doesn't trade at the same
price on all markets - The law of one price. Two assets with identical cash flows don't trade at the same price.
Asset with a known price in future does not today trade at its future price discounted at a risk-free interest rate (or
the asset doesn't have negligible costs of storage; eg, this cond holds for grain but not for securities). Arbitrage is
buying a product in 1 market and selling it in another for a high price at a later time. The transaction must occur
simultaneously to avoid exposure to market risk (prices change before trans is done). In the simplest example, any
good sold in one market should sell for the same price in another. Traders may, eg, find wheat prices lower in ag
regions than cities, purchase the good, and transport it to another region to sell at a higher price (ignores transport,
storage, risk, etc.). True arbitrage requires that there be no market risk involved. Where securities are traded on
more than one exchange, arbitrage occurs by simultaneously buying in one and selling on the other.
Mean reversion: identify the trading range for a stock, compute the avg price wrt assets, earnings, etc. When
current price < avg price, stock is considered attractive for purchase - expecting the price will rise. When
market price > average price, market price expected to fall. I.e., deviations from avg price are expected to
revert to the average.
STD of most recent prices (e.g., last 20) is often a buy/sell indicator. Stock reporting services (Yahoo!
Finance, MS Investor, Morningstar, etc.), offer moving avgs for periods eg 50,100 days. Identifying high/low
prices for the study period is still necessary.
Scalping (trading) is a method of arbitrage of small price gaps created by the bid-ask spread. Scalpers attempt
to act like traditional market makers or specialists. To make the spread means to buy at the Bid price and sell
at the Ask price, to gain the bid/ask difference. This procedure allows for profit even when the bid and ask do
not move at all, if traders are willing to take market prices. It normally involves establishing and liquidating a
position quickly-in min/sec. A scalper is a market makers who maintains liquidity and order flow of a product
of a market. A market maker is a specialized scalper (volume of trades are much more than the scalpers.) and
has a trading sys to monitor activity, bound by exchange rules while individ trader is not.
Transaction cost reduction: Most Algo Trading strategies are cost-reducers. Large orders are broken into
smaller orders and entered over time ("iceberging"). Success is measured by avg purchase price against
VWAP. Alg design finds hidden orders or icebergs is called "Stealth".
Strategies that only pertain to dark pools: Recently, HFT, comprises a broad set of buyside as well as
market making sell side traders has become prominent and controversial.[25] These algs or techniques are
commonly given names eg "Stealth" (devel by Deutsche Bank), "Iceberg", "Dagger", "Guerrilla", "Sniper",
"BASOR" Dark pools are alternative elect stock exchanges where trading is anonymous, with most orders
hidden or "iceberged."[28].
Gamers or "sharks" sniff out large orders by "pinging" small market orders to buy and sell. When several
small orders are filled sharks discover presence of a large iceberged order. "It’s an arms race,” Everyone is
building more sophisticated algs.
High-frequency trading: HFT firms represent 2% of the approx 20,000 firms operating today, but
account for 73% of all equity trading volume.[31] As of the first quarter in 2009, total assets under
management for hedge funds with HFT strategies were US$141 billion, down about 21% from their
high.[32] The HFT strategy was first made successful by Renaissance Technologies.[33] High-frequency
funds started to become especially popular in 2007 and 2008.[32] Many HFT firms are market makers
and provide liquidity to the market which has lowered volatility and helped narrow Bid-offer spreads
making trading and investing cheaper for other market participants.[32][34][35] HFT has been a subject
of intense public focus since the U.S. SEC and the Commodity Futures Trading Commission implicated
both algorithmic and HFT in the May 6, 2010 Flash Crash.[11][12][13][14]. HFT is quantitative trading
that is characterized by short portfolio holding periods (Wilmott (2008), Aldridge (2009)). There are 4
categories of HFT strategies: market-making based on order flow, market-making based on tick data info,
event arbitrage & statistical arbitrage.All portfolio allocation decisions are made by computerized
quantitative models. The success of HFT strategies is largely driven by their ability to simultaneously
process volumes of info, something ordinary human traders cannot do.
Market making is a set of HFT strategies involving placing a limit order to sell (or offer) above the
current market price or a buy limit order (or bid) below the current price in order to benefit from the bidask spread. AutoTrading Desk, bought by Citigroup 7/07, has been an active market maker accounting for
6% of total volume on both NASDAQ &NY Stock Exchange.[36]
Statistical Arbitrage: Classical arbitrage strategy might involve several securities such as covered
interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic
bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward
contract on the currency. If the market prices are sufficiently different from those implied in the model to
cover transaction cost then 4 transactions can be made to guarantee a risk-free profit. HFT allows similar
arbitrages using models of greater complexity involving many more than 4 securities. The TABB Group
estimates that annual aggregate profits of low latency arbitrage currently exceed $21 billion.[5]
Event Arbitrage: A subset of risk, merger, convertible, or distressed securities arbitrage that
counts on a specific event (contract signing, regulatory approval, judicial decision...) to change
price of two or more financial instruments and permit the arbitrageur to earn a profit.[37]
Merger arbitrage also called risk arbitrage would be an example of this. Merger arbitrage
generally consists of buying the stock of a company that is the target of a takeover while shorting
the stock of acquiring company. Usually the market price of the target company is less than the
price offered by the acquiring company. The spread between these two prices depends mainly on
the probability and the timing of the takeover being completed as well as the prevailing level of
interest rates. The bet in a merger arbitrage is such a spread will eventually be zero, if and when
the takeover is completed. The risk is that the deal "breaks" and the spread massively widens.
Low-latency trading: HFT is often confused with lo-latency trading that uses computers that
execute trades within ms, or "with extremely low latency" in jargon of trade. Lo-latency trading
is highly dependent on ultra-low latency networks. They profit by providing info, such as
competing bids and offers, to their algs ms faster than their competitors.[5] The revolutionary
advance in speed has led to the need for firms to have a real-time, colocated trading platform in
order to benefit from impl high- frequency strategies.[5] Strategies are constantly altered to
reflect the subtle changes in the market as well as to combat the threat of the strategy being
reverse engineered by competitors. There's also a very strong pressure to continuously add
features or improvements to a particular alg, such as client specific modifications and various
perf. enhancing changes (benchmark trading perf, cost reduction for the trading firm or a range of
other impls). This is due to the evolutionary nature of algorithmic trading strategies - they must
be able to adapt and trade intelligently, regardless of market conditions which involves being
flexible enough to withstand a vast array of market scenarios. As a result, a significant proportion
of net revenue from firms is spent on R&D of these autonomous trading sys.[5].
Strategy Implementation: Most algo strategies implemented using modern prog langs, tho some
still implement strategies designed in spreadsheets. Basic models can rely on as little as a linear
regression, while more complex game-theoretic and pattern recognition[38] or predictive models
can also be used to initiate trading. Neural nets and GAs used to create these models.
Issues and developments:
Algo trading improves market liquidity[39] among other benefits. Improvements in productivity
brought by algo trading have been opposed by human brokers/traders facing competition from
computers
Concerns: “The downside with these systems is their black box-ness,” Mr. Williams said. “Traders
have intuitive senses of how the world works. But with these systems you pour in a bunch of
numbers, and something comes out the other end, and it’s not always intuitive or clear why the
black box latched onto certain data or relationships.”[29] “The Financial Services Authority has
been keeping a watchful eye on the development of black box trading. In its annual report the
regulator remarked on the great benefits of efficiency that new technology is bringing to the market.
But it also pointed out that ‘greater reliance on sophisticated technology and modeling brings with it
a greater risk that systems failure can result in bus interruption’. ”[40]. UK Treasury minister Lord
Myners warned companies could become "playthings" of speculators because of automatic highfrequency trading. Lord Myners said the process risked destroying the relationship between an
investor and a company.[41]. Other issues include the technical problem of latency or the delay in
getting quotes to traders,[42] security and the possibility of a complete system breakdown leading to
a market crash.[43] Algorithmic and HFT were implicated in the May 6, 2010 Flash Crash,[11][13]
when the DJIA plunged about 600 points only to recover those losses within minutes. At the time, it
was 2nd largest point swing, 1,010.14 points, and the biggest one-day point decline, 998.5 points, on
an intraday basis in DJIA history.
Recent Developments: Financial market news is now being formatted by firms such as Need To Know News,
Thomson Reuters, Dow Jones, and Bloomberg, to be read and traded on via algorithms."Computers are now
being used to generate news stories about company earnings results or economic statistics as they are released.
And this almost instantaneous information forms a direct feed into other computers which trade on the
news."[46].
The algs do not simply trade on simple news stories but also interpret more difficult to understand news. Some
firms are also attempting to automatically assign sentiment (deciding if the news is good or bad) to news stories
so that automated trading can work directly on the news story.[47]. "People are looking at all forms of news
and building their own indicators around it in semi- structured way," as they constantly seek out new trading
advantages said a global director of strategy at Dow Jones Enterprise Media Group. His firm provides both a
low latency news feed and news analytics for traders. Passarella also pointed to new academic research being
conducted on the degree to which frequent Google searches on various stocks can serve as trading indicators,
the potential impact of various phrases and words that may appear in SEC Commis statements and the latest
wave of online communities devoted to stock trading topics.[47].
Technical designs of such systems are not standardized. Conceptually, design can be divided into logical units:
1. Data stream unit (part of sys that receives data (e.g. quotes, news) from external sources). 2. The decision or
strategy unit 3. The execution unit. With wide use of social networks, some sys impl scanning or screening
techs to read posts of users extracting human sentiment/influence the trading strategies. [49]
Effects: Though its development may have been prompted by decreasing trade sizes caused by decimalization,
algo trading has reduced trade sizes further. Jobs once done by human traders are being switched to computers.
Speeds of computer connections, in millsec and even micros, have become very important.[50][51]. More fully
automated markets such as NASDAQ, Direct Edge and BATS, in the US, have gained market share[52] from
less automated markets such as the NYSE. Economies of scale in electronic trading have contributed to
lowering commissions and trade processing fees, and contributed to int'l mergers and consolidation of financial
exchanges. Competition is developing among exchanges for the fastest processing times for completing trades.
For example, in June 2007, the London Stock Exchange launched a new system called TradElect that promises
an average 10 millisecond turnaround time from placing an order to final confirmation and can process 3,000
orders per second.[53]